This invention relates generally to pattern recognition, and, more particularly to measuring the performance of region of interest (ROI) identification algorithms.
Due to the ever increasing volume of postal items and packages being delivered, postal services and delivery services are increasingly relying on optical character recognition to recognize the addresses of the items to be delivered. In order to effectively recognize the addresses of items to be delivered, it is necessary to identify where the address information is located on the postal item. The first step in that identification is the identification of a region of interest or an area of interest which can be examined in order to determine whether the area or region is an address block.
A variety of algorithms have been utilized to identify a region of interest in an item to be delivered, such as a mail piece. Neural network algorithms have been disclosed as algorithms to identify a region of interest (see for example, the algorithm for generating address block candidates described in U.S. Pat. No. 6,014,450). If a neural network algorithm is used for region of interest identification, it is necessary to train the network. The network “learns” during training by comparing the output to a known output and adjusting the weights to reduce the error (see for example, S. K. Rogers, M. Kabrisky, An Introduction to Biological and Artificial Neural Networks for Pattern Recognition, SPIE, Bellingham, Wash., 1991, p.100). Thus, a measure of the error in identifying a region of interest is desired when a neural network algorithm is utilized.
A genetic algorithm could also be used for region of interest identification. In the development of a genetic algorithm, it is sometimes useful to utilize case based learning (see, for example, K. Rasheed, H. Hirsh, “Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization”, Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97), Morgan Kaufmann, San Francisco, Calif., 1997,url=“citeseer.nj.nec.com/73094.html”.) A number of known designs and a measure of the fitness of the solution obtained by the genetic algorithm are needed.
Even further, when algorithms such as the algorithm for detecting Areas of Interest (AOI) found in M. Wolf et al., “Fast Address Block Location in Handwritten and Printed Mail-piece Images”, Proc. Of the Fourth Intl. Conf. on Document Analysis and Recognition, vol.2, pp.753-757, Aug. 18-20, 1997, or the segmentation methods defined in P. W. Palumbo et al., “Postal Address Block Location in Real time”, Computer, Vol. 25, No. 7, pp. 34-42, July 1992, are utilized, a measure of the performance of the algorithm allows the comparison of two or more algorithms. Thus, there is a need for a method and system for measuring the performance of region of interest identification algorithms.